Early people propose detection algorithms [1]}, [2]}, [3]}, [4]} based on classic image forensics technology, which use the inherent statistics of the image, such as local noise features [1]}, statistical features [3]}, Photo-Response [2]}, device fingerprints [4]}, and quality evaluation. With the success of deep learning, methods of using neural networks to automatically extract features for deepfake detection are proposed. Most current deep forgery detection algorithms treat this problem as a common two-classification problem [9]}, [10]}, [11]}. However, the high fidelity of fake face images makes it more suitable for fine-grained classification [12]}, [13]}, [14]}.
